Dear All,
I have the following code which works fine but takes 16+ hours to run and I
was hoping someone could give me a hint as to how I could make it more
efficient.
Let me explain what the code is supposed to do. Each single row of the m.o.f
matrix (n*p dim) has to go through the following operation Dm*m.o.f[i]^alpha1m
where both Dm and alpha1m have n*1 dimensions. Each row of the m.o.f is to be
evaluated n times for the different values of alpha1m and Dm. My code changes
the Dm and alpha1m (n*1) matrices into (n*p) matrices (not shown below, done
only 1 time) and also change the (1*p) m.o.f row into a m.o (n*p) matrix (shown
below, done n times). For each of the n rows of the m.o.f matrix we repeat this
operation which we record in the temp matrix. The third line of the loop
multiplies for each row, the columns of the matrix and takes the resultant mean
of the vector.
I know I should use the whole object approach somehow but after many tries I
gaave up for the long approach. I know the explanation above is rather long and
I apologize for that, any hint/help will be greatly appreciated.
for (i in 1:numrows)
{
mo.o<-t(array(m.o.f[i,],dim=c(numcols,numrows)))
t<-Dm*m.o^alpha1m
l1[i]<-mean(apply(t,1,"prod"))
}
Best Regards,
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